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QA Exam 1 > QA EXAM 1 > Flashcards

QA EXAM 1 Flashcards

1
Q

two types of samples, and they are both

A

homogeneous and heterogeneous, both random, will divide total area into grid and assign numbers that can be chosen at random

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2
Q

homogeneous material sample

A

(speckled) x number of randomly selected samples from total available area

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3
Q

heterogeneous material sample

A

(blobs) randomly selected but comprise a representative area of each part

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4
Q

concentration

A

quantity of solute in a given volume (or mass) of solvent or solution

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5
Q

molarity (M)

A

number of moles of solute per 1 L of solution

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6
Q

molality (m)

A

number of moles of solute per 1 kg of solvent

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7
Q

why would molality (m) (moles/kg) be better sometimes?

A

mass does not change, so it is valuable to use when temperature or volume change could occur

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8
Q

why would molarity (M) (moles/L) be better sometimes?

A

better for precise volumes of solutions, and is particularly useful in reaction stoichiometry and lab settings where solutions are prepared and measured volumetrically, providing a practical measurement

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9
Q

percent composition

A

the ratio of the amount of each element to the total amount of individual elements present in the compound multiplied by 100

weight % = (mass of solute/mass of solution) x 100%
volume % = (volume of solute/volume of solution) x 100%

(ppm and ppb)

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10
Q

ppm

A

g of solute/10e6 g of solution

unitless quantity

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11
Q

ppb

A

g of solute/10e9 g of solution

unitless quantity

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12
Q

molarity to % comp

A

% comp = (molarity x molar mass of solute)/(molar mass of solution) x 100%

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13
Q

molarity to ppm or ppb

A

ppm = Molarity × Molar mass of solute × 10e6
ppb = Molarity × Molar mass of solute × 10e9

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14
Q

molarity

A

M = n/V

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15
Q

sig figs (addition and subtraction)

A

least decimal places

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16
Q

sig figs (multiplication and division)

A

least sig figs

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17
Q

fundamental limitations (uncertainties in measurements)

A

physical and philosophical, theoretical principles and universal constraints like laws

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18
Q

practical limitations (uncertainties in measurements)

A

imperfect instrumentation (electrical noise), measured object is a dynamic system (solvent evaporation), human factor (imperfect)

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19
Q

ideal (perfect) measurement

A

no such thing, there is always error, experimental error, in every measurement

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20
Q

systematic (determinate) error

A

reproducible error due to a flaw in the design of an experiment or imperfections of the equipment

it can be discovered and (in favorable cases) corrected

examples: poor instrument calibration, inadequate standards

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21
Q

random (indeterminate) error

A

irreproducible error; arises from the effects of uncontrolled (and often uncontrollable) variable in the measurements

it cannot be corrected; must be dealt with using statistics

examples: electrical noise in instrument, human factors

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22
Q

how can systematic (determinate) errors be reduced?

A

improving experimental techniques, selecting better instruments and removing personal bias as far as possible

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23
Q

how can random (indeterminate) errors be reduced?

A

repeated measurements, using a large sample, controlling extraneous variables

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24
Q

standard deviation

A

measures how closely the data points are clustered around the mean

describes precision, NOT accuracy

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25
Q

average value (arithmetic mean), x̄

A

the sum of the measured values divided by the number of measurements

26
Q

confidence interval

A

How likely it is that the true mean, μ, lies within a certain distance from the measured mean, x̄. It is an estimate of uncertainty and can be used to compare results from different experiments

27
Q

confidence interval trends

A

more measurements, smaller confidence interval, smaller st dev, better confidence that the average value x̄ is essentially the true value

28
Q

confidence interval math (s, n and t)

A

s: standard deviation
n: number of observations
t: student’s t from table

29
Q

calibration

A

a procedure for connecting the response of an analytical instrument to the quantity of analyte being measured

done to maintain accuracy and improve repeatability in measurements

30
Q

calibration curve

A

Method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration.

Do not extrapolate beyond the curve! At some point, it will go flat, you do not know what the curve looks like beyond what you have.

31
Q

calibration curve set up

A
  • use a set of standards covering a range of concentrations
  • measure response for each standard several times, and subtract the data from a blank sample (matrix effect)
  • plot a graph of instrument responses versus the analyte concentration, then use the least squares procedure to find the best straight line
  • when analyzing an unknown, run a blank again and obtain the corrected response
32
Q

matrix effect **

A

The matrix is everything in the unknown, other than analyte. A matrix effect is a change in the analytical signal caused by anything in the sample other than analyte.

33
Q

dynamic range

A

whole range

full range my instrument gives a measurably different response (curved)

the range of concentrations an instrument can read, from the minimum to the maximum detectable

the concentration range over which there is a measurable response to analyte, even if the response is not linear.

34
Q

linear range

A

flat

range of input or output values for which an electronic amplifier produces an output signal that is a direct, linear function of the input signal

analyte concentration range over which response is proportional to concentration.

35
Q

method of least squares

A

a mathematical technique that allows the analyst to determine the best way of fitting a curve on top of a chart of data points

approach: minimize only y-deviations of experimental data points from the “best” straight line

(x: quantity of analyte, y: instrument response)

36
Q

method of least squares assumptions

A

1) y-errors are greater than x-errors (reasonable, since x-values represent standards)
2) uncertainties of all y-values are similar

(x: quantity of analyte, y: instrument response)

37
Q

false positive

A

test result that is incorrectly classified as positive

38
Q

false negative

A

test result that is incorrectly classified as negative

39
Q

detection limit

A

the lowest concentration of the analyte that can be reliably detected

is a reflection of the precision of the instrumental response obtained by the method when the concentration of the analyte is zero

50% of measurements will give false negatives if a sample contains analyte at the detection limit

40
Q

spike

A

a sudden and significant increase or peak in data points, often indicating outliers or anomalies within the dataset, which may require further investigation to understand their underlying causes and implications.

41
Q

selectivity

A

(also called specificity) means being able to distinguish analyte from other species in the sample (avoiding interference).

42
Q

sensitivity

A

is the capability of responding reliably and measurably to changes in analyte concentration

43
Q

accuracy determination

A

comparison with a reference standard, calibration

44
Q

precision determination

A

repeatability, st dev and variance (lower values mean higher precision)

45
Q

spike recovery calculation

A

c: concentration

46
Q

what does spike tell us about the matrix

A

anomaly detection

47
Q

detection limit **

A

the smallest quantity of analyte that is “significantly different” from the blank

3 st dev = 100% curve

48
Q

signal detection limit vs minimal detectable concentration

A

The signal detection limit (SDL) represents the smallest analyte signal distinguishable from background noise and is expressed in units corresponding to the INSTRUMENT’S measurement (e.g., volts for voltage-based instruments).

The minimum detectable concentration (MDC) denotes the lowest concentration or amount of analyte detectable in a sample and is expressed in CONCENTRATION units (e.g., parts per million) reflecting the sample’s volume or mass.

49
Q

standard addition

A

known quantities of the analyte are added to the unknown

A technique in which an analytical signal due to an unknown is first measured. Then a known quantity of analyte is added, and the increase in signal is recorded. From the response, it is possible to calculate what quantity of analyte was in the unknown.

Matrix limitation. Samples kept.

50
Q

why is standard addition good?

A

Standard additions are used when complicated matrix makes it difficult to measure just one desired analyte.

For example, if you wanted to know the amount of a specific compound in beer, this would be difficult using a classic calibration curve, since there are a lot of different compounds present & the one you’re looking for might not give a clear signal. We use [X]f/([S]f + [X]f) = Ix/(Is+x) to solve for your desired analyte concentration, where I is instrument response, x is unknown/analyte & s is the sample being added.

By increasing the concentration of the compound you want to measure, you gradually increase the signal and it becomes much easier to distinguish which signal is coming from your compound.

51
Q

standard addition and matrix

A

Standard addition is especially useful when the sample’s complexity or unknown composition interferes with the analytical signal, a situation known as matrix effects. By adding known amounts of the analyte directly to the sample, standard addition helps isolate the analyte’s signal from the interference caused by matrix components, ensuring accurate quantification.

52
Q

assumptions of standard addition

A

1) linear response to analyte
2) matrix has the same effect on added analyte as it has on the analyte in the original unknown

53
Q

internal standards

A

A known amount of the internal standard (a compound which is different from the analyte but has similar properties) is added to the unknown. Signal from analyte is compared with signal from the internal standard to find out how much analyte is present.

54
Q

why are internal standards good?

A
  • Instrument limitation mitigation. This method helps us assess and deal with problems with instrument reproducibility without worrying about (as much?) sample loss.
  • Very useful if the quantity of sample analyzed or instrument response varies from run to run for uncontrollable reasons, like chromatography and NMR.
  • Very useful when sample loss occurs prior to analysis. The same fraction of both standard and sample are lost together

Internal standards are typically used when the instrument used to measure a sample has varying responses that make it difficult to generate a normal calibration curve. We use this to find the instrument response factor, F, where Ax/[X]=F*As/[S] (x & s are your unknown & standard, A is response). We find F by measuring a series of As for known [S], then use this to find [x].

55
Q

internal standards vs standard addition

A

Internal standards are used when the nature of the instrument causes a fluctuating output, which would prevent a good linear calibration.

Standard additions are used when you have a really complex mixture and the signal of a specific compound is hard to distinguish/very low.

56
Q

internal standard assumption

A

the relative response of an instrument to analyte and standard remains constant over a range of concentrations, usually a valid assumption

57
Q

solubility product

A

an equilibrium constant for a reaction in which a solid salt dissolves to yield its constituent ions in solution

58
Q

common ion effect

A

a salt will be less soluble if one of the constituents is already present in solution (but not always)

59
Q

conjugate acids and bases

A

related to each other by a gain/loss of one H+.

conj base: acid that lost H+
conj acid: base that gains H+

60
Q

pH

A

measure of acidity or basicity of a solution

61
Q

systematic treatment required here

A

Figure 9-1 Calculated pH as a function of concentration of strong acid or strong base in water.

At intermediate concentrations of 10e6 to 10e8 M, the effects of water ionization and the added acid or base are comparable. Only in this region is a systematic equilibrium calculation necessary.

systematic treatment of equilibrium: A method that uses the charge balance, mass balance(s), and equilibria to completely specify a system’s composition.

62
Q

assumptions in weak acid pH problem

A

if we assume that dissociation of HA is much greater than H2O, then [A-]&raquo_space; [OH-], and “effectively” all [H+] are derived from the dissociation of weak acid HA, not water itself. therefore [H+] = [A-].

is this assumption good?: if there’s high concentrations of weak acid, large dissociation constant (Ka) or low pH, yes

if the solution is extremely dilute, there’s a low dissociation of weak acid (Ka), or specific special cases, then no

QA Exam 1 (2 decks)

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